Method and apparatus for improving cognitive performance

ABSTRACT

A method for delivering electrical stimulation to alter a cognitive state of a user, the method comprising: monitoring a brain signal from the user via one or more intracranial electrodes implanted in the brain of the user while the user is presented with a stimulus; comparing the brain signal to a testing phase biomarker, wherein the testing phase biomarker is derived from a cognitive test performed on a contributor during a testing phase; delivering electrical stimulation to a brain of the user based on the comparing step to steer the brain of the user towards a high performance cognitive state.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a U.S. National Stage Entry of InternationalPatent Application No. PCT/US2016/014438, entitled “Method and Apparatusfor Improving Cognitive Performance,” filed on Jan. 22, 2016, which inturn claims the benefit of U.S. Provisional Patent Application No.62/107,358, entitled “Method and Apparatus for Improving CognitivePerformance,” filed on Jan. 24, 2015 and U.S. Provisional PatentApplication No. 62/238,871, entitled “Method and Apparatus for ImprovingCognitive Performance,” filed on Oct. 8, 2015, all of which areincorporated by reference herein in their entireties.

GOVERNMENT SUPPORT

The present invention was made with Government support under Grant No.N66001-14-2-4032 awarded by Space and Naval Warfare Systems Center,Pacific. The Government has certain rights in the invention.

BACKGROUND

The present invention generally relates to a method and apparatus forimproving cognitive performance through the application of brainstimulation.

SUMMARY

In one embodiment there is a method for creating a biomarker indicativeof high-performance or low-performance cognition, the method comprising:presenting a stimulus to a contributor; receiving a response and a brainsignal from the contributor; associating the response and the brainsignal with the stimulus; determining that the brain signal correspondsto one of: a high-performance cognitive state of the contributor and alow-performance cognitive state of the contributor; and generating abiomarker using the brain signal that corresponds to the one of: thehigh-performance cognitive state of the contributor and low-performancecognitive state of the contributor.

In a further embodiment, the biomarker is a set of features (such as thetime-frequency decomposition of the voltage traces recorded across anarray of electrodes) that distinguish a brain signal corresponding to ahigh-performance cognitive state from a brain signal corresponding to alow-performance cognitive state.

In a further embodiment, the method further comprising: transforming abiomarker with a large set of features into a biomarker comprising oneor several numbers, which distinguishes a brain signal corresponding toa high-performance cognitive state from a brain signal corresponding toa low-performance cognitive state.

In a further embodiment, the biomarker is a threshold corresponding to afeature of the brain signal.

In a further embodiment, the method further comprising: transmitting thebiomarker to a modulation device.

In a further embodiment, the method further comprising: receiving thebrain signal from a modulation device connected to a brain of thecontributor.

In a further embodiment, the method further comprising: associating theresponse with the stimulus by comparing the response to the stimulus.

In a further embodiment, the method further comprising: associating thebrain signal with the stimulus by determining whether a time periodwhere the brain signal is monitored by a modulation device overlaps witha time period where the stimulus is presented to the contributor.

In a further embodiment, if the response matches the stimulus, theresponse is a positive response.

In a further embodiment, the brain signal corresponds to the highperformance cognitive state of the contributor if the brain signal isassociated with a stimulus having a positive response or a fast reactiontime.

In a further embodiment, if the response does not match the stimulus,the response is a negative response.

In a further embodiment, the brain signal corresponds to the lowperformance cognitive state of the contributor if the brain signals areassociated with a stimulus having a negative response or a slow reactiontime.

In a further embodiment, the high performance cognitive state is anaccurate memory.

In a further embodiment, the low performance cognitive state is aninaccurate memory.

In a further embodiment, the method further comprising: storing thebiomarker in a database.

In a further embodiment, the brain signal is one or more brain signalsfrom one or more contributors.

In one embodiment, there is a system for creating a biomarker indicativeof high performance or low performance cognitive state according to anyof the methods in the preceding claims.

In one embodiment, there is a non-transitory computer readable storagemedium having stored thereon computer-executable instructions which,when executed by a processor, perform any of the methods for creating abiomarker indicative of high performance or low performance cognitivestate in the preceding claims.

In one embodiment, there is a method for delivering stimulation to altera cognitive state of a user, the method comprising: monitoring a brainsignal from the user (optionally while the user is presented with astimulus); comparing the brain signal to a testing phase biomarker,wherein the testing phase biomarker is derived from a cognitive testperformed on a contributor during a testing phase; deliveringstimulation to a brain of the user based on the comparing step to steerthe brain of the user towards high performance cognitive state.

In one embodiment, there is a method to optimize the location andparameters of stimulation to alter the cognitive state of the user, themethod comprising: monitoring a brain signal from the user; stimulatingthe user's brain at varied locations using varied stimulationparameters; comparing the user's brain response with the testing phasebiomarker to identify the optimal stimulation location and parameters.

In a further embodiment, the testing phase biomarker is indicative of alow performance cognitive state of the user as determined based on acognitive test performed on the contributor.

In a further embodiment, the testing phase biomarker is indicative of ahigh performance cognitive state of the user as determined based on acognitive test performed on the contributor.

In a further embodiment, the contributor is the user.

In a further embodiment, the contributor is a plurality of contributors.

In a further embodiment, the contributor is different than the user.

In a further embodiment, the method further comprising: updating thetesting phase biomarker based on the brain signal of the user and aresponse of the user to the stimulus.

In a further embodiment, electrical stimulation is delivered to a singlesubfield of a hippocampus.

In a further embodiment, electrical stimulation is delivered to multipleregions of the brain of the user.

In one embodiment, there is a system for delivering electricalstimulation to alter a cognitive state of a user according to any of themethods in the preceding claims.

In one embodiment, there is a non-transitory computer readable storagemedium having stored thereon computer-executable instructions which,when executed by a processor, perform any of the methods for deliveringelectrical stimulation to alter a cognitive state of a user in thepreceding claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description of embodiments of the invention willbe better understood when read in conjunction with the appended drawingsof an exemplary embodiment. It should be understood, however, that theinvention is not limited to the precise arrangements andinstrumentalities shown.

In the drawings:

FIG. 1 illustrates examples of how different brain regions are activatedto perform a brain function according to at least one embodiment of theinvention;

FIG. 2 is a conceptual diagram illustrating an exemplary system that canmonitor brain signals and/or deliver stimulation to a user to improvebrain functionality of the user according to at least one embodiment ofthe invention;

FIG. 3 illustrates a flow chart of a method for creating a biomarkerindicative or associated with a desired state of performance of a givenuser for a given cognitive task according to at least one embodiment ofthe invention;

FIG. 4 illustrates a flow chart for delivering stimulation to improvecognition (e.g.

memory cognition) according to at least one embodiment of the invention;

FIG. 5 is a functional block diagram illustrating components ofmodulation device according to at least one embodiment of the invention;

FIG. 6 illustrates an example of neural activity in a brain while a useris attempting to accurately create a memory over time according to atleast one embodiment of the invention;

FIG. 7 illustrates an example of neural activity in a brain while a useris attempting to accurately create a memory according to at least oneembodiment of the invention;

FIG. 8 illustrates an exemplary map of potential performance improvementthat can be gained from biomarkers at various time points andfrequencies according to at least one embodiment of the invention; and

FIG. 9 illustrates potential performance gains available by reinstatingthe biomarkers associated with various quartiles of performanceaccording to at least one embodiment of the invention.

FIG. 10A illustrates a delayed free recall task performed by subjectswhile local field potentials (LFPs) from surface and depth electrodesimplanted subdurally according to at least one embodiment of theinvention.

FIG. 10B illustrates a time-frequency spectral decomposition performedon the recordings from each electrode during all word presentationperiods according to at least one embodiment of the invention. In thisembodiment, mean power across the encoding period within each frequencyband was used as input to a logistic regression classifier trained todiscriminate whole-brain spectral patterns predictive of later recallfrom later forgetting.

FIG. 10C illustrates the delayed free recall task performed by thesubjects during a later session, while targeted electrical stimulationwas delivered to an adjacent pair of electrode contacts according to atleast one embodiment of the invention. In this embodiment, the trainedlogistic regression model was applied to the whole-brain LFP datarecorded just prior to delivery of stimulation, and the model'sestimates of encoding efficiency at the time of stimulation delivery wasused to assess the effects of stimulation on memory performance.

FIGS. 11A-J illustrate graphs showing logistic regression classifierperformance data for two example subjects and the group according to atleast one embodiment of the invention.

FIGS. 11A and 11D illustrate graphs showing classifier outputprobability for an eight-list period of the delayed free recall taskaccording to at least one embodiment of the invention. In thisembodiment, gray shaded regions correspond to the top and bottomterciles of the distribution of classifier probability estimates; medianindicated by the dashed line. In this embodiment, red shaded portionscorrespond to later recalled words; and blue shaded portions correspondto later forgotten words.

FIGS. 11B and 11E illustrate graphs showing area under the curve (AUC)for both subjects was significantly greater than chance (estimated usinga permutation procedure in which words were randomly assigned torecalled/not-recalled category), in accordance with at least oneembodiment of the invention.

FIGS. 11C and 11F illustrate graphs showing example subject recallperformance represented as percentage deviation from the (subject) mean,separated by tercile of the classifier's encoding efficiency estimatefor each encoded word, according to at least one embodiment of theinvention.

FIG. 11G illustrates a graph showing average AUC was significantlygreater than chance across the group (P<0.0001), according to at leastone embodiment of the invention.

FIG. 11H illustrates a graph showing average absolute classifier weightsacross patients for broad regions of interest, according to at least oneembodiment of the invention, where All=all electrodes; FC=frontalcortex; PFC=prefrontal cortex; TC=temporal cortex; MTL=medial temporallobe (including hippocampus, amygdala and cortex); HC=hippcampus;OC=occipital cortex; PC=parietal cortex.

FIG. 11I illustrates a graph showing change in recall performance fromthe top to bottom tercile of classifier estimates, as a function of timeand frequency, according to at least one embodiment of the invention.

FIG. 11J illustrates a group mean tercile plot showing significantlydecreased performance for words that the classifier assigned to thelowest encoding efficiency bin compared to words assigned to the highestbin (P<0.0002), according to at least one embodiment of the invention.

FIG. 12A illustrates a graph showing recall probability as a function ofserial position (top) and inter-item lag (bottom) do not significantlydiffer as a function of stimulation condition, according to at least oneembodiment of the invention.

FIG. 12B illustrates a graph showing stimulation's effect on memoryperformance varied across subjects (mean change in normalized percentrecall=8.9 9.3%, p>0.33), according to at least one embodiment of theinvention.

FIG. 12C illustrates a graph showing the behavioral effect ofstimulation correlated with the change in encoding efficiency estimatedby the classifier (r(25)=0.33, p<0.02, permutation test), according toat least one embodiment of the invention. Dashed line indicates meanpermutation-derived regression line.

FIG. 13 illustrates a graph showing the effects of stimulation depend onthe encoding state at the time of delivery, according to at least oneembodiment of the invention. Stimulation's effect on memory is afunction of the encoding efficiency at the time stimulation is applied,according to at least one embodiment of the invention. When encodingefficiency was low, stimulation tended to increase memory relative tomatched intervals in the NoStim condition (p=0.06), according to atleast one embodiment of the invention.

DETAILED DESCRIPTION I. Overview

The human brain is composed of billions of neurons electricallyinterconnected and organized into various areas to perform a variety offunctions. The electrical activation and/or deactivation of neurons orgroups of neurons is largely responsible for the function of the brainand communication amongst the various areas of the brain along thenetworks.

In some instances, brain stimulation can be therapeutically applied inorder to prevent the onset of or treat an undesirable state, such as inthe cases of epilepsy or tremors associated with Parkinson's Disease. Inneurological disease, there is typically a clear separation between‘normal’ and ‘abnormal’ brain function. This is the case, for example,in epilepsy where abnormal function is marked not only by seizures, butalso by neurophysiological markers of epileptic network activity (e.g.,spikes and sharp waves) that are present even when the brain is notseizing.

In the absence of neurological disease, brain functions can be highlyvariable across time. In fact, it is common for the brain to fluctuatebetween states along a spectrum of low-performance cognitive state(s) tohigh-performance cognitive state(s) for a given user while a cognitivetask is being performed or when different cognitive tasks are performedover time, yet all of these cognitive states from high-performancecognitive states to low-performance cognitive states may be classifiedas “normal” brain function. This fluctuation between a high-performancecognitive state and a low-performance cognitive state may occur on amoment-by-moment, trial-by-trial and even day-by-day basis. For example,a user might be asked to remember a list of twelve words, but only beable to recall six words at a later date. One of the reasons forincomplete recall may be the user fluctuating between a high-performancecognitive state and a low-performance cognitive state while trying toremember each word or patterns of words at the time of memorization, atthe time of recall or both.

It is generally thought that the activation of numerous neurons, amongother types of network function, may be necessary to carry out eachbrain function when performing a cognitive task (e.g., creating amemory, solving a puzzle, recall of earlier memorized information,etc.). FIG. 1 illustrates examples of how different brain regions areactivated to perform a brain function according to at least oneembodiment of the invention. This example illustrates the primary stagesof a memory task: memory encoding, memory retrieval and vocalization. Asshown, different brain regions are activated during different stages,and even sub-stages such as early encoding and late encoding. Activationfor memory encoding, retrieval and vocalization is represented throughshading of the brain areas, where regions exhibiting statisticallysignificant increased or decreased activation are shaded in dark gray.The brain signals associated with each stage can be used as biomarkersindicative of accurate or inaccurate memory formation, as explainedbelow in more detail.

Therefore, to alter cognition in embodiments of the invention, neuralactivity and/or engagement may be tracked while a user is performing acognitive task and stimulation may be delivered to a user to activate ordeactivate populations of neurons associated with the desired state ofperformance (e.g., high performance or low-performance cognitive states)for a given cognitive task. For example, a method or device may detect alow-performance cognitive state associated with increased likelihood ofmemory loss and stimulate portions of the brain at appropriatelyselected targets to transition the brain to an enhanced or highperformance cognitive state to diminish the likelihood of memory loss.Alternatively, a method or device may detect a high-performancecognitive state associated with increased likelihood of memory creationand stimulate in the brain at appropriately selected targets to prolongthe length of time that the brain is in the high-performance cognitivestate to thereby increase the potential of the user to correctly performthe cognitive task, among other things.

The fluctuation or variability between high-performance cognitive statesand low-performance cognitive states exists in all people, such as thosewith normal brain function as well as those with brain impairments(e.g., Alzheimer's disease). In other words, even for a user with normalbrain function, that user may fluctuate between high-performancecognitive states and low-performance cognitive states for a given taskor tasks. Therefore, in order to improve cognitive performance, it isnot enough to simply stimulate the brain to reduce symptoms of brainimpairments. Instead, it is necessary to determine whether a user is ina high-performance cognitive state or a low-performance cognitive statefor a given task or tasks by comparing brain activity (e.g., theelectrical signals that may be recorded from electrodes as well as otherbiomarkers of brain function such the concentration ofneurotransmitters) of a user to one or more biomarkers indicative of ahigh-performance cognitive state or a low-performance cognitive stateregardless of whether or not the user has brain impairment. Byidentifying the target for an intervention (i.e., modulate the brainwhen it is in a certain cognitive state) and monitoring the variabilityin the brain state, one can guide the user's brain to a desiredcognitive state using brain stimulation. In addition, it is alsopossible to guide the user's brain to an enhanced-performance cognitivestate, in which the user performs beyond his or her normal limits.

In one embodiment, the use of a system or device of the presentinvention includes a multi-phase process to ultimately alter cognitionof a user.

In the first phase, known as the testing phase, a cognitive test ispresented and cognitive tasks (e.g., memory exercises/games) areperformed by one or more biomarker contributors. Biomarkers of themeasured brain signals are assessed during the cognitive tasks bycorrelating these brain signals with task performance, and identifying aset of one or more biomarkers that predict task performance (e.g.amplitude, band power, phase, etc.). These biomarkers may be saved in amemory or entered into a database. The biomarkers may also be associatedwith a level of cognitive performance of the given contributor (e.g.,enhanced-performance cognitive state, high-performance cognitive stateor low-performance cognitive state) for a given cognitive task basedupon the test results. While performance and biomarkers indicative ofthe performance may be contributor specific, in some embodiments similarbiomarkers indicative of performance during a given cognitive taskacross multiple contributors may also be created. Alternatively, custombiomarker(s) may also be created where the custom biomarker(s) iscreated without the use of input or testing from a biomarkercontributor. Alternatively, modified or hybrid biomarker from one ormore contributors can be created.

In some embodiments, if a large set of biomarkers is identified, the setmay be reduced to one or more biomarker representative values using oneor more dimensionality reduction algorithms (such as linear classifiers,support vector machines, neural networks, etc.) to classify the brainsignals into enhanced-performance, high-performance and low-performancecognitive states. In some embodiments, the number of biomarkerrepresentative values is less than 25, less than 20, less than 15, lessthan 10, less than 5, less than 4, less than 3, or 1.

In some embodiments, there is an intermediate phase, known as thestimulation optimization phase, where one or more electricalstimulations may be applied to the biomarker recipient's brain at variedlocations and using varied stimulation parameters (such as amplitude,frequency, pulse width, etc.) to determine the effects of each parameterset on the set of biomarkers identified in the testing phase. In someembodiments, stimulation locations and parameter sets may be evaluatedby comparing the recipient's brain response to a particular location andparameter set with the set of biomarkers identified in the testingphase.

In the next phase, known as the modulation phase, electrical stimulationis applied to a brain of a biomarker recipient to modulate therecipient's level of cognitive performance. In one embodiment, theinitial set of one or more stimulation locations and parameters forelectrical stimulation are chosen in the stimulation optimization phasebased on the degree to which the stimulation locations and parametersproduced a match (e.g., correlation) between the recipient's brainsignals during the stimulation optimization phase and a set of one ormore biomarkers identified in the testing phase.

In some embodiments, the amount and location of electrical stimulationis based on an earlier developed brain signal biomarker pattern orbiomarkers patterns or a contemporaneously developed biomarker that isindicative of a desired level of cognitive performance for a givencognitive task to thereby enforce or alter a desired level ofperformance of the recipient.

Specifically, electrical stimulation can be applied to a recipient,where the stimulation imparts a pattern of electrical activity in therecipient's brain (e.g., such as increased amplitude of a particularbrain wave, or increased synchronicity between brain waves at two ormore loci) corresponding to a set of biomarkers having an associatedperformance level. Therefore, the recipient's brain activity can bemodulated to match the desired biomarkers, leading to an improvement inperformance during the cognitive task.

The stimulation optimization phase can be repeated as often as needed tooptimize the effectiveness of the modulation phase in improving therecipient's cognitive performance.

In one embodiment, the altered cognition resultant from the modulationphase may be improved cognition and/or ability to perform a givencognitive task. In another embodiment, the altered cognition may beimpaired cognition. In one embodiment, the biomarker may be indicativeof supra-normal (i.e. enhanced) performance for a given cognitive taskfor a given user. One of ordinary skill in the art would appreciate thatthe systems and methods described herein could be used for the purposesof improving or impairing cognition.

Referring to the drawings in detail, wherein like reference numeralsindicate like elements throughout, there is shown in FIGS. 1-13, systemsand methods, generally designated, in accordance with exemplaryembodiments of the present invention.

II. Definitions

Biomarker—In one embodiment, a biomarker is a characteristic of one ormore brain signals (e.g., activation/deactivation of neurons, electricalpotential changes in the brain or chemical changes in the brain) fromone or more contributors that indicates the presence of a particularbrain state (e.g., enhanced performance cognition, high performancecognition or low performance cognition).

Cognitive task—In one embodiment, a cognitive task is a task thatrequires at least one mental process and for which a performance metriccan be calculated (e.g. accuracy or reaction time).

High performance cognitive state—In one embodiment, a high performancecognitive state is a brain state associated with favorable performancein a cognitive task (e.g. high accuracy or fast reaction time). In oneembodiment, the spectrum of low-performance cognitive state(s) tohigh-performance cognitive state(s) can be a statistical distribution(e.g. a normal distribution characterized by a mean and standarddeviation, or a skewed distribution like the gamma or ex-Gaussian whichoften describes variation in performance with a fixed lower bound). Insome embodiments, a high-performance cognitive state may be anycognitive state that is in the top X-th percentile of the range, where Xcould be any value in the range of 0-100, along the spectrum oflow-performance and high-performance cognitive states. In someembodiments, a high-performance cognitive state may be defined inrelation to the variability of the distribution, as in the case of anormal distribution where such states could be defined in terms of theirstandard deviations from the mean (e.g., performance exceeding the mean+X standard deviations, where X could be 3, 2, 1, 0, −1, −2, 3, etc.).

Low performance cognitive state—In one embodiment, a low performancecognitive state is a brain state associated with unfavorable performancein a cognitive task (e.g. low accuracy or slow reaction time). In oneembodiment, the spectrum of low-performance cognitive state(s) tohigh-performance cognitive state(s) can be a statistical distribution(e.g. a normal distribution characterized by a mean and standarddeviation, or a skewed distribution like the gamma or ex-Gaussian whichoften describes variation in performance with a fixed lower bound). Insome embodiments, a low-performance cognitive state may be any cognitivestate that is in the bottom X-th percentile of the range, where X couldbe any value in the range of 0-100, along the spectrum oflow-performance and high-performance cognitive states. In someembodiments, a low-performance cognitive state may be defined inrelation to the variability of the distribution, as in the case of anormal distribution where such states could be defined in terms of theirstandard deviations from the mean (e.g., performance inferior to themean+X standard deviations, where X could be 3, 2, 1, 0, −1, −2, −3,etc.).

Enhanced performance cognitive state—In one embodiment, anenhanced-performance cognitive state is a brain state associated withsupra-normal performance in a cognitive task (e.g. above normal accuracyor reaction time limits). In one embodiment, the spectrum oflow-performance cognitive state(s) to enhanced-performance cognitivestate(s) can be a statistical distribution (e.g. a normal distributioncharacterized by a mean and standard deviation, or a skewed distributionlike the gamma or ex-Gaussian which often describes variation inperformance with a fixed lower bound). In some embodiments, anenhanced-performance cognitive state may be any cognitive state above ahigh-performance state and that is in the top X-th percentile of therange, where X could be any value in the range of 0-100. In someembodiments, an enhanced-performance cognitive state may be anycognitive state above a high-performance state and defined in relationto the variability of the distribution, as in the case of a normaldistribution where such states could be defined in terms of theirstandard deviations from the mean (e.g., performance exceeding themean+X standard deviations, where X could be 3, 2, 1, 0, −1, −2, −3,etc.)

Contributor—In one embodiment, a contributor is a user that suppliesbrain signals in association with a cognitive task to facilitatecreation of one or more biomarkers.

Recipient—In one embodiment, a recipient is a user that receiveselectrical stimulation based on the one or more biomarkers from one ormore contributors.

User—In one embodiment, a user is either a contributor or a recipient.

III. System Overview

FIG. 2 is a conceptual diagram illustrating an exemplary system 100 thatcan monitor brain signals and/or deliver stimulation to a user 110 toimprove brain functionality of the user 110 according to at least oneembodiment of the invention. System 100 may include a testing device 120configured to present a cognitive test to a user 110. System may alsoinclude modulation device 130 configured to monitor brain signals from auser 110 and/or deliver stimulation to a user 110 based on biomarkersidentified from the cognitive test results.

Testing device 120 is configured to conduct a cognitive test on a user110 in at least one of the two phases described herein. In the firstphase, known as the testing phase, user 110 may represent one or morebiomarker contributors. In the second phase, known as the modulationphase, user 110 may represent one or more recipients. In someembodiments, user 110 may be a contributor and a recipient. In someembodiments, a contributor and a recipient may be different users.

In one embodiment, testing device 120 is a computing device including aprocessor and memory that includes instructions that, when executed,cause the processor to implement the cognitive test. Examples ofcomputing devices may include personal computers and smart phones, amongothers.

Testing device 120 may further include one or more human-machineinterfaces, such as sensory interface 122 and/or input device 124 to aidin conducting the cognitive tests.

Testing device 120 may further include a communication interface totransfer data between testing device 120 and modulation device 130.Examples of communication interfaces may include a modem, a networkinterface (such as an Ethernet card), a communication port, a PersonalComputer Memory Card International Association (PCMCIA) slot and card,etc. Data transferred via the communication interface may be in the formof signals, which may be electronic, electromagnetic, optical, or othersignals capable of being transmitted or received by communicationinterface.

In one embodiment, the cognitive test includes one or more stimuli andone or more responses. In one embodiment, a cognitive test invokesvariability in cognitive performance when user 110 performs this task.In one embodiment, stimuli are instructions posed to user 110 forperforming a cognitive task (e.g., remember a word). In one embodiment,responses are provided by user 110 in response to stimuli (e.g.,recalling a word user 110 was asked to remember).

In the testing phase, one or more stimuli may be presented to user 110via sensory interface 122. One example of a stimulus may involve thepresentation of a word that the user 110 is asked to remember. Inalternative embodiments, a stimulus may be based on sight, sound, touch,taste or smell. For example, instead of presenting words, the stimulusmay be a symbol or picture. Alternatively, the stimulus might be acertain sound or smell that the contributors are asked to remember andlater recall, recognize, or produce some other memory judgment on thetarget item.

Sensory interface 122 may be configured to communicate information fromtesting device 120 to user 110 via communication link 126. While sensoryinterface 122 in FIG. 2 is a graphical user interface showing the word“cat” (i.e., a stimulus) for a cognitive test, other human-machineinterfaces may be used to communicate the cognitive test from testingdevice 120 to user 110. For example, output device 112 may be a speakeror a device to test touch, smell or taste.

Communication link 126 may communicate stimulus data from testing device120 to sensory interface 122 via a wired or wireless connection usingstandard communication protocols.

Input device 124 may be configured to process responses for thecognitive test from user 110. Examples of input devices may include akeyboard, mouse, joystick, touch screen, microphone, an eye fixationtracking device and/or a stylus. The responses may be necessary toverify whether the user 110 performed the cognitive task. For example,it may not be known whether a word is remembered or forgotten by a user110 until s/he is prompted to remember each word and provide a responseat a later time. If the response matches the stimulus, then user 110(e.g., a contributor) may be in an enhanced-performance or highperformance cognitive state. If the response does not match thestimulus, then user 110 (e.g., a contributor) may be in alow-performance cognitive state.

Communication link 128 may communicate response data received by inputdevice 124 to testing device 120 via a wired or wireless connectionusing standard communication protocols.

Modulation device 130 may be configured to monitor electrical, magneticand/or chemical brain signals of user 110 and/or deliver electrical,magnetic and/or chemical stimulation to brain regions of the brain ofuser 110. It should be noted that in view of the teachings herein, oneof skill in the art understands that modulation device 130 can incertain instances both sense brain signals and stimulate parts of thebrain. In one embodiment, modulation device 130 is a deep brainstimulation device. In one embodiment, modulation device 130 is ACTIVA®PC+S, developed by Medtronic, Inc. In one embodiment, modulation device130 is RNS® System, developed by NeuroPace, Inc. One embodiment of themodulation device 130 is described in more detail in FIG. 5.

Modulation device 130 may further include one or more electrodes, suchas electrode 132, which are implanted in the brain of user 110. The oneor more electrodes sense the electrical signals in the brain that areprovided to modulation device 130 via lead 134.

In one embodiment, modulation device 130 may be configured to monitorbrain signals of user 110 during the cognitive test and, optionally,provide monitored brain signal data (i.e. first monitored brain signaldata) of user 110 to testing device 120 via communication link 136.

Communication link 136 may communicate the monitored brain signal datareceived by modulation device 130 to testing device 120 via a wired orwireless connection using standard communication protocols.

Using the stimulus data, the response data and monitored brain signaldata, testing device 120 may process the data to identify biomarkersindicative of an enhanced-performance cognitive state, ahigh-performance cognitive state or a low-performance cognitive state ofthe user 110 and generate biomarker data. Biomarker data may betransmitted from testing device 120 to modulation device 130 usingcommunication link 138. In alternative embodiments, modulation device130 may identify biomarkers using the stimulus data, the response dataand/or monitored brain signal data of the user 110 that can later beused to determine how to deliver stimulation to the brain of user 110.In this embodiment, stimulus data and/or response data may betransmitted from testing device 120 to modulation device 130 usingcommunication link 138.

Communication link 138 may communicate the biomarker data, the stimulusdata and/or the response data from testing device 120 to modulationdevice 130 via a wired or wireless connection using standardcommunication protocols. In an alternative embodiment, communicationlink 136 and communication link 138 may be one bi-directionalcommunication link.

In one embodiment, modulation device 130 may receive the biomarker data,the stimulus data and/or the response data from testing device 120, maystore the data in memory associated with modulation device 130. In oneembodiment, modulation device 130 may receive the stimulus data and/orthe response data and generate the biomarkers before storing thebiomarkers in memory.

In the modulation phase, in association with a cognitive test presentedto a user 110 (e.g., recipient) by testing device 120, modulation device130 may receive monitored brain signal data from user 110. Modulationdevice 130 may then compare the monitored brain signal data (i.e.,second monitored brain signal data) to a biomarker and deliver certainstimulation to the brain of the user 110 based on the biomarker and themonitored brain signal data. In some embodiments, modulation device 130may generate a modulation phase biomarker from the monitored brainsignal data (i.e., second monitored brain signal data) to compare to thebiomarker generated during the testing phase (i.e. testing phasebiomarker).

In various embodiments, certain stimulation may be delivered bymodulation device 130 to a brain of user 110 targeted for maintaining anenhanced or a high-performance cognitive state if the brain signalsindicate an enhanced or a high-performance cognitive state is present inthe brain of user 110. Alternatively, certain stimulation may bedelivered by modulation device 130 to a brain of user 110 targeted forcreating an enhanced or high-performance cognitive state if the brainsignals indicate the presence of a low-performance cognitive state inthe brain of user 110.

In various embodiments, modulation device 130 includes a plurality ofleads (e.g., lead 134) including a plurality of electrodes (e.g.,electrode 132). In various embodiments, certain brain signals may bemonitored by one or more electrodes of one or more leads. In variousembodiments, electrical stimulation may be delivered to one or moreelectrodes of one or more leads to drive the brain of user 110 to anenhanced or high performance cognitive state.

In alternative embodiments, testing device 120 and/or modulation device130 may include different combinations of functions, software and/orhardware as described in system 100 and the document herein. Inalternative embodiments, different testing devices (e.g. testing device130) may be used to present cognitive tests to one or more contributorsand/or one or more users. In alternative embodiments of the invention,different modulation devices (e.g., modulation device 130) may be usedto monitor brain signals of one or more contributors and/or one or moreusers. In alternative embodiments, different modulation devices (e.g.,modulation device 130) may be used to deliver stimulation to neurons ofthe brain of one or more users. In some embodiments, system 100 mayimplement the testing phase and/or the modulation phase as describedherein.

IV. Testing Phase

FIG. 3 illustrates a flow chart of a method 300 for creating a biomarkerindicative or associated with a desired state of performance of a givenuser for a given cognitive task according to at least one embodiment ofthe invention.

At step 310, a cognitive test may be presented to one or morecontributors (e.g. user 110) using testing component 120. In oneembodiment, the cognitive test includes a cognitive task (e.g., memorygame) to be performed by one or more contributors.

In one embodiment, the cognitive test may include one or more stimuliand require one or more contributor responses. First, one or morestimuli may be presented to the one or more contributors. In oneembodiment, the one or more stimuli are presented to the one or morecontributors via sensory interface device 122, as described herein. Inone example of a cognitive test, a contributor might be asked toremember, and later recall, twelve words. After the test, thecontributor might remember six of the words and forget the other sixwords.

At step 320, testing device 120 may receive one or more responses fromthe contributor and may associate each response with a stimulus. In oneembodiment, the response is received from the contributor via inputdevice 124, as described herein. In one embodiment, testing device 120may associate each response with a stimulus by matching the response tothe stimulus. For example, if the contributor is asked to remember a setof twelve words, the contributor might recall six of the words. If oneof the presented words is “cat” and the contributor provides a responseof “cat” using input device 124, testing device 120 may match the twowords together using a standard comparison technique. In this instance,if the response matches the stimulus, testing device 120 may determinethat a positive response was provided to testing device 120 from thecontributors for the stimulus and associate the positive response withthe stimulus. For the six words that the contributor cannot recall orwhere a response is provided that does not match any of the stimuli,testing device 120 may determine that a negative response was providedto testing device 120 from the contributor and associate the negativeresponse with the stimulus. Other quantitative indices of performancethat may be used to define biomarkers include measures of the dynamicsof recall including clustering on the basis of temporal, semantic andspatial similarities among items, inter-response times, and thesimilarity of error responses to the target items in the studied list.In an alternative embodiment, a contributor may be presented with avirtual environment through the sensory interface 122 and be instructedto navigate to particular locations indicated by visual landmarks (“thetarget”). The contributor may later be asked to navigate to thoselocations without the presence of the landmark. The contributor'sperformance may then be characterized as high-performance orlow-performance depending upon how close to the target they got and howlong it took them to travel to the target location.

At step 330, during the time period around when each stimulus ispresented to the contributor, one or more brain signals of the one ormore contributors may be monitored and received by modulation device 130via one or more electrodes (e.g., electrode 132) implanted within thebrain of the contributor. The brain signals may be subsequently receivedby testing device 120 after transmission by modulation device 130.

At step 340, testing device 120 may associate each of the monitoredbrain signals with a corresponding stimulus. In one embodiment, testingdevice 120 associates the stimulus and the monitored brain signal bycomparing a time period where modulation device 130 monitored the brainsignals to a time period when the stimulus was presented to acontributor by testing device 120. If the time periods overlap, thentesting device 120 associates the monitored brain signals with thestimulus. For example, the testing device 120 may display a stimulussuch as the word “cat” on a display to a contributor from a time t to atime t+5 seconds and then may display the word “dog” on a display to acontributor from a time t+6 seconds to a time t+11 seconds. Inconjunction, modulation device 130 may monitor brain signals from time tto time t+11 seconds. Testing device 120 may be configured to associateall brain signals from time t to time t+5 seconds with the stimulususing the word “cat” and associate all brain signals from time t+6seconds to a time t+11 seconds with the stimulus using the word “dog” sothat testing device 120 can later be used to identify the biomarkers.

At step 350, testing device 120 may determine the one or more brainsignals that correspond to an accurate memory (i.e. enhanced orhigh-performance cognition) or an inaccurate memory (i.e.low-performance cognition) of the contributor. In one embodiment, theone or more brain signals that correspond to a stimulus having a corrector fast response from the contributor may be considered one or morebrain signals that correspond to an accurate memory state of thecontributor. In some embodiments, testing device 120 may determine theone or more brain signals that correspond to an inaccurate memory of thecontributor. In one embodiment, the one or more brain signals thatcorrespond to a stimulus having an incorrect or slow response from thecontributor may be considered one or more brain signals that correspondto an inaccurate memory state of the contributor.

At step 360, testing device 120 may generate one or more testing phasebiomarkers using one or more brain signals that correspond to anaccurate memory of the contributor and/or the brain signals thatcorrespond to an inaccurate memory of the contributor. The one or moretesting phase biomarkers may be generated from the received brainsignals based on one or more characteristics that distinguish one ormore brain signals corresponding to enhanced or high-performancecognition from one or more brain signals corresponding tolow-performance cognition. Further, the one or more testing phasebiomarkers may be generated from the received one or more brain signalsbecause the one or more brain signals corresponding to an accuratememory (i.e. enhanced or high-performance memory cognition) may share afirst set of one or more common characteristics that are distinguishablefrom one or more brain signals corresponding to an inaccurate memory(i.e. low-performance memory cognition) that may share a second set ofone or more common characteristics. For example, the one or more testingphase biomarkers might be based on patterns that emerge in the one ormore brain signals, such as energy content within a particularbioelectrical frequency band, morphological patterns, consistent periodof oscillation, and/or changes in bioelectrical amplitude or frequency,for example. In addition, the testing phase biomarker might be based ona similar frequency, bioelectrical oscillation frequency-band power,and/or phase of oscillation of the brain signals that correspond to anaccurate memory of the contributor.

In one embodiment, a testing phase biomarker may correspond to one ormore brain signals from different populations of neurons or brainregions over one or more time periods (i.e. a spatio-temporalbiomarker). FIG. 6 illustrates an example of electrical activity in abrain while a contributor is attempting to accurately create a memoryover time according to at least one embodiment of the invention. Eachrow of brain images represents a visual depiction of the electricalactivity in the brain of a contributor during a certain time period. Forexample, the first row of brain images represent the brain of thecontributor at −750 ms to 250 ms, the time period before and immediatelyafter a stimulus (e.g. a word shown on a visual display) has begun to beshown to a contributor. “Word on” represents when a word is first shownto a contributor while “word off” represents when a word is no longershown to a contributor. The second through fifth rows of brain imagesrepresent the brain of the contributor from 0 ms to 1700 ms (i.e. theperiod when the contributor is shown a stimulus). The seventh row ofbrain images represent the brain of the contributor at 1600 ms to 2100ms (i.e. the period immediately preceding when the contributor is nolonger shown the stimulus to the period after the contributor is shownthe stimulus). As time progresses, different brain areas are activatedas the contributor attempts to accurately create a memory, as reflectedin the increase in high-frequency activity measured from intracraniallyimplanted electrodes in that region. Here, statistically reliable memoryencoding related high-frequency signals, which have been shown tocorrelate positively with neuronal spiking, appear as grayscale shadingoverlaying different areas of the brain. The signals measured in theseareas of the brain (e.g., the high frequency activity in FIG. 6) thatreliably predict the goodness of memory function based on theircorrelation with stimulus encoding events and subsequent memoryperformance constitute a particular biomarker of good memory encoding inthe contributor. Individual biomarkers, such as these, combinemathematically to create multivariate indices of memory function whichwe refer to more generally as testing phase biomarkers in the subsequentsections. Note that testing phase biomarkers refer to biomarkersgenerated during an assessment of cognitive function, and that thesebiomarkers may be uniquely determined for different aspect of cognitiveperformance such as memory encoding, memory retrieval, reinstatement ofthe context of previously learned information, or cognitive operationsthat are crucial for perception, attention, learning, memory or decisionmaking.

In one embodiment, a testing phase biomarker may correspond to one ormore brain signals from one or more brain regions. FIG. 7 illustrates anexample of the brain's electrical activity as a contributor isattempting to accurately create a memory according to at least oneembodiment of the invention. In this embodiment, the biomarker is onlyspatio-dependent, meaning dependent on the location of the neurons, andnot the time period when the neurons are activated, are needed to createa biomarker. Here, neural activity, which is significantly correlatedwith memory performance, is shown using grayscale shading. For thesesignificant areas of the brain of a contributor, one or morecharacteristics of the brain signals are analyzed and correlated tocreate a testing phase biomarker. These brain signals may be weighted,correlated and/or combined using different signal processing techniquesto create a testing phase biomarker.

In some embodiments, testing device 120 may generate one or morebiomarker representative values. If a large set of testing phasebiomarkers is identified, the set may be reduced to a one or morebiomarker representative values using one or more dimensionalityreduction algorithms (such as linear classifiers, support vectormachines, neural networks, etc.) to classify the brain signals intoenhanced-performance cognition, high-performance cognition andlow-performance cognition states. In some embodiments, each of the oneor more biomarker representative values may correspond to a value alonga range (e.g., 0 to 1). In these embodiments, certain values along therange may correspond to low-performance cognition (e.g., 0),high-performance cognition (e.g., 0.5) and enhanced-performancecognition (e.g., 1).

In some embodiments, testing device 120 may generate one or morestimulation optimization parameters corresponding to one or morebiomarkers or biomarker representative values. In some embodiments, theelectrical stimulation parameters may be optimized during a stimulationoptimization phase. In these embodiments, one or more electricalstimulations may be sequentially applied to the biomarker recipient'sbrain at varied locations and using varied stimulation parameters (suchas amplitude, frequency, pulse width, etc.) to determine the effects ofeach parameter set on the set of testing phase biomarkers or biomarkerrepresentative values. In some embodiments, the stimulation locationsand signal parameters for subsequent electrical stimulation during themodulation phase are chosen in the stimulation optimization phase basedon the degree to which one or more stimulation locations and parametersproduce a desired effect on the recipient's brain, such as causing amatch (e.g., correlation) between the recipient's brain signals duringthe stimulation optimization phase and the set of biomarkers orbiomarker representative values identified in the testing phase toimprove memory.

This stimulation optimization phase can be repeated as often as neededto optimize the effectiveness of the modulation phase in improving therecipient's cognitive performance. For example, the recipient'scognitive performance may change over time. In response, the stimulationoptimization phase may be repeated to adjust the electrical stimulationparameters to cause the desired effect on the recipient's brain.

Turning back to FIG. 3, at step 370, the testing phase biomarker may betransmitted from testing device 120 to modulation device 130, where thetesting phase biomarker may be stored in memory of the modulation device130. In some embodiments, the biomarker representative values and/orstimulation optimization parameters may be transmitted from testingdevice 120 to modulation device 130. Testing device 120 may also storethe testing phase biomarker, biomarker representative values and/orstimulation optimization parameters in memory (e.g. a database) forsubsequent us by others, including modulation device 130.

In alternative embodiments, modulation device 130 may include one ormore components of testing device 120 to implement the method 300. Inalternative embodiments, modulation device 130 may generate testingphase biomarkers using the stimulus data, the response data as well asmonitored brain signal data. In these embodiments, stimulus data andresponse data is transmitted from testing device 120 to modulationdevice 130. In these embodiments, modulation device 130 may receive thestimulus data and/or the response data and may generate the testingphase biomarkers before storing the testing phase biomarkers in memory.

The testing phase biomarkers will be used during a subsequent or secondcognitive test presented to a recipient to determine whether thesubsequently or second monitored brain signal data (e.g., modulationphase biomarker) correspond to an accurate memory of the contributor andwhether modulation device 130 will need to deliver stimulation to thebrain of the recipient as described for FIG. 4. It would be readilyapparent to anyone skilled in the art that data collected in thissubsequent testing phase can be used to further refine the biomarkers.This later phase could thereby be used to “tune” the stimulationparameters to the biomarkers as they may drift over time.

V. Modulation Phase

FIG. 4 illustrates a flow chart 400 for delivering stimulation toimprove cognition (e.g. memory cognition) according to at least oneembodiment of the invention.

At step 410, modulation device 130 may monitor a brain signal of therecipient (e.g., user 110). In one embodiment, modulation device 130 maymonitor a brain signal of the recipient via one or more electrodes(e.g., electrode 132) implanted within the brain of the recipient.

In some embodiments, a plurality of brain signals may be monitored bymodulation device 130. In various embodiments, a brain signal may beindicative of neural activity. In various embodiments, a modulationdevice 130 may measure a brain signal of a brain region.

In some embodiments, modulation device 130, or another device, mayidentify and/or derive modulation phase biomarkers using the brainsignals monitored by modulation device 130 during the modulation phase.

At step 420, modulation device 130 may compare the one or more brainsignals, or modulation phase biomarkers, to one or more testing phasebiomarkers derived from a cognitive test performed on a contributor asdescribed herein. In one embodiment, the brain signal is compared to atesting phase biomarker indicative of an enhanced or high-performancecognition as determined based on a cognitive test performed on acontributor. In another embodiment, the brain signal is compared to atesting phase biomarker indicative of low-performance cognition asdetermined based on a cognitive test presented to a contributor. In oneembodiment, modulation device 130 may retrieve one or more testing phasebiomarkers stored in memory of modulation device 130 and/or testingdevice 120.

In one embodiment, a testing phase biomarker is a thresholdcorresponding to a characteristic of a brain signal corresponding to anenhanced or high-performance cognition or a brain signal correspondingto low-performance cognition. If the testing phase biomarker is athreshold biomarker, the stimulation may be triggered for the recipientif the brain signals of the recipient indicate transition from anenhanced or high-performance cognition to low-performance cognition, orvise versa. Alternatively, if the testing phase biomarker is a thresholdbiomarker, the stimulation may be triggered for the recipient under thecondition that the brain signals of the recipient indicate that therecipient is maintaining enhanced or high-performance cognition orlow-performance cognition.

In some embodiments, modulation device 130 may compare the one or morebrain signals, or modulation phase biomarkers, to one or more biomarkerrepresentative values derived during the testing phase, as describedherein. In some embodiments, modulation device 130 may first derivemodulation phase biomarker representative values and compare thesebiomarker representative values to the biomarker representative valuesderived during the testing phase.

At step 430, modulation device 130 may deliver certain stimulation(e.g., electrical, chemical, magnetic) to the brain of the recipientbased on the comparison of the brain signal determined during themodulation phase to one or more testing phase biomarkers (oralternatively, based on the comparison between the modulation phasebiomarker representative values and the testing phase biomarkerrepresentative values). In one embodiment, modulation device 130 maydeliver certain electrical stimulation to the brain of the recipient viathe one or more electrodes (e.g., electrode 132).

In various embodiments, certain stimulation may be delivered bymodulation device 130 to a brain of a recipient targeted for maintainingenhanced or high-performance cognition if the brain signals indicateenhanced or high-performance cognition of the brain of the recipient. Invarious embodiments, certain stimulation may be delivered by modulationdevice 130 to create enhanced or high performance cognition if the brainsignals indicate low-performance cognition of the brain of therecipient, or vice versa. For example, the stimulation may transitionthe brain of the recipient from a low performance cognitive state to anenhanced or high-performance cognitive state, or vice versa.

In some embodiments, modulation device 130 may deliver certainstimulation based on the stimulation optimization parameters determinedduring the stimulation optimization phase, described herein.

In some embodiments, delivering stimulation to the brain of therecipient may be user-specific. For example, the modulation device 130may stimulate the brain in one recipient using different parameters fromthose used to stimulate the brain of a second recipient. Because ofthese user-specific differences in the effect of delivered stimulation,in some instances, where stimulation is applied to a recipient that doesnot realize the full effect of the stimulation, the stimulation may notcreate or maintain enhanced or high-performance cognition in the brainof the recipient. To properly provide adequate stimulation, modulationdevice 130 may adjust stimulation applied to the brain of the recipientto achieve a desired result (e.g., maintain enhanced or high-performancecognition) based on predetermined user-specific parameters. In someembodiments, modulation device 130 may first apply stimulation to arecipient using an initial set of stimulation parameters (such asfrequency, pulse width, amplitude, etc.), monitor the physiologicalresults of stimulation and determine a new user-specific set ofstimulation parameters by comparing the results of stimulation with thedesired enhanced- or high-performance brain state. This process may berepeated on an ongoing basis during the modulation phase, as it ispossible that a response of a recipient to stimulation may vary overtime.

In alternative embodiments, the testing phase described in method 300may overlap with the modulation phase described in method 400. If thephases overlap, then monitored brain signal data of a recipient and thecorresponding responses of the recipient to stimuli can be used toupdate the one or more testing phase biomarkers generated in step 360.

In some embodiments, the contributor is the same user as the recipient.By having the same user act as a contributor and a recipient, system 100may allow for real-time or contemporaneous updates to a testing phasebiomarker as stimulation is also applied to the user. In someembodiments, the contributor and the recipient are different users. Byhaving different users as contributors and recipients, potentialrecipients will not need to experience a testing phase before using thedevice to alter cognition, thereby saving time for the futurerecipients.

In some embodiments, modulation device 130 delivers stimulation to arecipient without first determining the current brain signalpattern/state of the recipient. In this way, the recipient's currentbrain signals can be overridden based on a biomarker to thereby alterthe brain signal pattern/state of the recipient without first waiting todetermine the current brain signal pattern/state of the recipient.

V. Description of Sensing and Modulation Device

FIG. 5 is a functional block diagram illustrating components ofmodulation device 130 according to at least one embodiment of theinvention. In this embodiment, modulation device 130 includes controlcircuitry components including processor 240, memory 241, stimulationgenerator 242, sensing module 244, switch module 246, communicationmodule 248, and power source 250.

Memory 241 may include any volatile or non-volatile media, such as arandom access memory (RAM), read only memory (ROM), non-volatile RAM(NVRAM), electrically erasable programmable ROM (EEPROM), flash memory,and the like. Memory 241 may store computer-readable instructions that,when executed by processor 240, cause modulation device 130 to performvarious functions described herein. Memory 241 may include operatinginstructions 256 executable by the processor 240 for causing modulationdevice 130 to carry out the functions referenced herein. Memory 241 maystore stimulation instructions as part of stimulation programs 252 thatare available to be selected by processor 240 in response to detectionof brain signals from the sensing module 244 and a comparison of thebrain signals to testing phase biomarkers stored in cognitive test data253. In addition, processor 240 may be configured to record diagnosticinformation, such as sensed signals, signal characteristics, brain stateepisode information, or the like in memory 241 or another memory orstorage device. The various functions and options described herein maybe performable automatically by modulation device 130 by processor 240execution of operating instructions 256, cognitive test data 253 and/orstimulation programs 252 stored in memory 241.

The steps, procedures, techniques, etc. referenced herein may be carriedout in part by, for example, software instructions, such as those usedto define a software or computer program. The computer-readable medium(e.g., memory 241) may store instructions (e.g., operating instructions256, cognitive test data 253 and stimulation programs 252) executable tocarry out the steps, procedures, techniques, etc, described herein.

Processor 240 may determine whether a monitored brain signal includes abiomarker (e.g., testing phase biomarker or modulation phase biomarker)indicative of enhanced-performance, high-performance or low performancecognition by comparing the brain signal to a testing phase biomarkerstored in cognitive test data 253. Processor 240 may analyze a monitoredbrain signal for correlation with a template, or a specific storedvalue. For example, the peak, lowest or average amplitude of the brainsignal may be compared to a threshold, the crossing of the thresholdindicating a new presence of enhanced-performance, high-performance orlow performance cognition.

Processor 240, as part of control circuitry, may be configured tocontrol stimulation generator 242 to deliver stimulation based on theresults of monitoring the brain signals. Processor 240, may beconfigured to control simulation generator 242 to deliver stimulationwith pulse voltage or current amplitudes, pulse widths, and frequencies(i.e., pulse rates), and electrode combinations specified by thestimulation programs 252 with predetermined delays, e.g., as stored inmemory 241. In some embodiments, processor 240 may control stimulationgenerator 242 to deliver a substantially continuous stimulation waveformrather than pulsed stimulation. In various embodiments, one or moreparameters of the stimulation may be changed according to the comparisonbetween the testing phase biomarker and the second monitored brainsignal data. Changing one or more parameters can include changing theenergy level of the stimulation, such as by adjusting frequency,amplitude, and/or duration of one or more pulses comprising thestimulation. Other parameters that can be changed include adjusting thetiming of a stimulation window or other timing parameter for delivery ofstimulation.

Processor 240, may include any of one or more of a microprocessor, acontroller, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), one or more gate arrays (e.g., afield-programmable gate array (FPGA)), discrete logic circuitry, and anynumber of each. The functions attributed to the control circuitry and/ora processor may be embodied as firmware, hardware, software or anycombination thereof specifically configured (e.g., with programming) tocarry out those functions.

Sensing module 244 is configured to sense brain signals of user 110(shown in FIG. 2) via a selected subset of the set of electrodes 132, orwith one or more electrodes of the set of electrodes 132. Processor 240may control switch module 246 to electrically connect sensing module 244to a selected subset of the set of electrodes 132, or with one or moreelectrodes of the set of electrodes 132. In this way, sensing module 244may selectively sense bioelectrical brain signals with differentcombinations of one or more electrodes. Although the electrodes 132 areprincipally described as being implanted within a brain in the manner ofDBS, other locations are additionally or alternatively contemplated. Forexample, electrodes 132 may be deployed at selected tissue sites or onselected surfaces of a human patient, such as on the brain, along thecortex, proximate the spinal cord, on the scalp, or elsewhere. As anexample, scalp electrodes may be used to measure or record EEG signals.As another example, electrodes implanted at the surface of the cortexmay be used to measure or record ECoG signals. In some embodiments, anexternal device may be worn with sensing elements positioned at adesired location adjacent the patient to detect a physiological signal(e.g., a brain signal).

Sensing module 244 may form part of a sensor circuit configured tomonitor a variety of signals via a variety of different sensingelements, such as a brain signals via electrodes 132 and/or otherphysiological signals. In some embodiments, sensing module 244 includescircuitry configured to measure one or more parameters of a brainsignal, such as amplitude, frequency or phase. Processor 240 may comparethe measured parameters of the brain signal (e.g., modulation phasebiomarker) to a testing phase biomarker stored in cognitive test data253. For example, processor 240 may determine whether the measuredparameters of the brain signal exceeds or falls below a thresholdrepresentative of the testing phase biomarker.

Processor 240 or other part of control circuitry may monitor brainsignals sensed by sensing module 244 in any suitable manner in order todetect and characterize enhanced or high-performance cognition orlow-performance cognition. For example, sensing module 244 may sense oneor more brain signals (e.g., a LFP (local field potential), neuralfirings, chemical changes in the brain) at one or more particular pointswithin a portion of the brain of user 110 that supports enhanced orhigh-performance cognition or low-performance cognition, and processor240 may monitor the brain signals. In one embodiment, processor 240 canmonitor brain signals and determine stimulation for user 110 inreal-time to maintain enhanced or high performance cognition for user110 based on a comparison of modulation phase biomarker derived frommonitored brain signals) to a testing phase biomarker.

Electrodes 132 can be used by sensing module 244 to sense the one ormore brain signals. The set of electrodes 132 of lead 134 may includeone or more of electrodes 132A, 132B, 132C, 132D, 132E, 132F. In oneembodiment, the number of electrodes (N) may be any whole number. In oneembodiment, N may be one of: one, two, three, four, five, six, seven,eight, nine, and ten. In one embodiment, N may be more than one and lessthan one hundred. In one embodiment, N may be more than one hundred andless than one thousand. In one embodiment, N may be more than onethousand and less than ten thousand.

Processor 240 may control switch module 246 to deliver electricalstimulation signals generated by stimulation generator 242 to eachelectrode of the set of electrodes 132. Processor 240 may control switchmodule 246 to monitor brain signals from each electrode of the set ofelectrodes 132 using sensing module 244.

In one embodiment, modulation device 130 includes a plurality of leads(e.g., lead 134). In one embodiment, each lead of the plurality of leadsincludes a set of electrodes (e.g., set of electrodes 132). In oneembodiment, lead 134 may include any combination of electrodes 132, suchas mesoscale electrodes (e.g., 132A, 132B, 132C, 132D) or radialelectrodes (e.g., 132E, 132F). Examples of different electrodes aredescribed in PCT Application No. PCT/IL02/00796, titled “ElectrodeSystem for Neural Applications,” and incorporated by reference herein inits entirety. In one embodiment, electrode 132 includes one or more gridelectrodes provided on the surface of the brain of user 110. In oneembodiment, electrode 132 is an intra-cranial electrode (e.g.,hippocampus electrode) that measures one or more brain signals of thehippocampus. In one embodiment, at least one of the electrodes 132 issized to sense a subfield of a brain region (e.g., hippocampus). In oneembodiment, a first lead of a plurality of leads may be positioned in afirst brain region of user 110 and a second lead of a plurality of leadsmay be positioned in a second brain region of user 110.

In various embodiments, certain electrical stimulation may be deliveredto certain electrodes of certain leads. In these embodiments, each leadincludes one or more sub-leads to individually connect one or moreelectrodes to processor 240. In various embodiments, electricalstimulation is delivered to neurons of the brain of user 110. In variousembodiments, electrical stimulation is delivered to a single subfield ofa hippocampus. In various embodiments, electrical stimulation isdelivered to multiple regions of the brain of user 110 simultaneously.

In one embodiment, mesoscale electrodes are spaced on a lead to maximizeconductive surface area of the portion of the lead implanted in thebrain of user 110. Such a configuration may maximize the number of brainareas that can be monitored and/or stimulated by modulation device 130.

In various embodiments, modulation device 130 may include one or moreexternal electrodes positioned on the outer surface of the cranium ofuser 110 that can sense and generate a bioelectrical brain signal thatcan be used to detect and characterize a brain signal of user 110. Suchdetection and characterization may use the techniques discussed hereinfor detecting and characterizing via internally sensed signals (e.g.,comparing signals, frequency or other parameter match, a biomarker,template, and/or other technique).

In various embodiments, modulation device 130 may include a transcranialmagnetic module, connected to and controlled by processor 240 to senseor stimulate electrical signals in the brain of user 110 via magneticinduction for similar purposes as described herein.

Communication module 248 may support wired or wireless communicationbetween modulation device 130 and testing device 120. Processor 240 mayreceive, as updates to sensing and/or stimulation programs, values forstimulation parameters such as amplitude and electrode combinationinformation from testing device 120, stimulus data and response datarelated to a cognitive test, via communication module 248. The updatesto the stimulation, sensing, or other programs may be stored withinstimulation programs 252 of memory 241. Stimulus data and response datamay be stored in cognitive test data 253. In one embodiment, modulationdevice 130 generates a testing phase biomarker using the monitored brainsignals, stimulus data and response data during the testing phase usingthe methods, functions and/or components described herein. In oneembodiment, modulation device 130 generates a modulation phase biomarkerusing the monitored brain signals during the modulation phase using themethods, functions and/or components described herein. Communicationmodule 248 may send data (e.g., brain signal data) to testing device 130on a continuous basis, at periodic intervals, or upon request fromtesting device 130.

Power source 250 may deliver operating power to various components ofmodulation device 130.

VI. Results

FIG. 8 illustrates an exemplary graph of potential performanceimprovement that can be gained from biomarkers at various time pointsand frequencies according to at least one embodiment of the invention.In this example, the scale bar on the right of the graph represents thepercentage improvement that can be gained using the techniques anddevices described herein. In this example, the percentage improvementmay range from zero to sixty. As shown in this example, when the devicesand techniques described herein are used at certain times and/orfrequencies, certain improvements can be achieved. For example, atregion 802, centered around time 850 ms at a frequency of 4 Hz, a 60%improvement in accurately creating memories was realized in users.

FIG. 9 illustrates potential performance gains available by reinstatingthe biomarkers associated with various quartiles of performanceaccording to at least one embodiment of the invention. This figureidentifies the potential performance gains that it is possible to obtainusing an embodiment of the one or more devices and methods described inthis patent application. These gains, such as the change from 1stquartile to 4th quartile showing approximately 100% improvement, areillustrative of the potential gains realized using one or more of thedevices and methods described herein.

VII. Example

Human episodic memory is dynamic, leading to satisfying periods of goodmemory as well as frustrating momentary lapses. Neural mechanisms thatare engaged when memoranda are encountered lead to encoding of memoryrepresentations in the brain. These mechanisms vary in their efficiency,which leads to variability in the likelihood of remembering informationlater on. Here, some embodiments describe use of targeted electricalstimulation of the human brain to modulate neural activity at encoding.In these embodiments stimulation's effect is measured on neural activityand use it to predict stimulation's effect on subsequent memoryperformance. Intracranial recordings are first collected whileneurosurgical patients studied lists of words for a later recall test.Using multivariate classification of neural activity, a model is fittedthat could discriminate between words likely to be later remembered vs.forgotten, and is applied to data collected in later stimulationsessions. When neural activity indicated recall was unlikely to besuccessful, stimulation improved performance, while the reverse was truewhen recall was predicted to be successful. The stimulation-evokedchange in the probability of memory success, estimated from neuralactivity, correlated with the stimulation-evoked change in memoryperformance. These findings indicate that memory success can be decodedfrom multivariate neural activity and can be predictably modulated withelectrical stimulation. The data suggest that electricalstimulation-based interventions could be used to therapeuticallymodulate memory dysfunction.

Memory function depends on encoding processes that lay downrepresentations of our experiences for long term storage, but theseprocesses vary in their efficiency from moment to moment. Recordings ofneural activity taken during encoding show that the response in manybrain areas differentiates information that is likely to be laterremembered from information likely to be forgotten. These differenceshave been observed in subcortical areas such as the hippocampus as wellacross cortex. The presence of subsequent memory effects (SMEs) acrossmany brain regions suggests that the coordinated activity of adistributed neural network is responsible for effective memory encoding.

If neural activity at encoding predicts variation in later memorysuccess, then modulation of neural activity at encoding should effectsubsequent memory performance. This assumption is the foundation ofattempts to use electrical stimulation of the brain to affect memoryperformance, but it has been difficult in humans to predict memoryoutcomes from the neurophysiological consequences of stimulation.Non-invasive methods like transcranial direct current stimulation (tDCS)have shown the potential to influence memory performance, but haveunclear neurophysiological mechanisms of action. Invasive methods suchas direct intracranial stimulation of the medial temporal lobe (MTL)have yielded inconsistent behavioral effects, with some studies showingimprovement and many others showing disruption. In some embodiments,neural activity collected across the brain is used and thatstimulation's effect on brain-network activity would predictstimulation's effect on memory performance.

In some embodiments, the approach was to use multivariate patternanalysis (MVPA) to derive estimates of encoding efficiency from neuralactivity. The relation was quantified between neural activity and theefficiency of memory encoding by recording electrical signals fromneurosurgical patients implanted with subdural and depth electrodes asthey performed a memory task. A machine learning classifier was trainedto discriminate between brain-wide patterns of neural activity thatpredicted later memory success from patterns that predicted later memoryfailure. This classifier was then tested on neural recordings collectedduring later independent sessions of the memory task. In these latersessions, electrical stimulation is applied to specific brain regions inorder to influence network activity. Brain stimulation is predicted tomodulate memory performance to the extent that it influenced physiologyacross the network. The classifier was used to decode neural activityrecorded before and after stimulation and examined howstimulation-induced changes in encoding efficiency predicted latermemory performance.

In some embodiments, intracranial electroencephalographic (iEEG) signalswere recorded from electrodes implanted in the brains of 32 subjects.Subjects performed a free recall memory test during which they studiedlists of 12 unrelated words followed by a 20 second mental arithmeticdistractor task. Subjects were then asked to freely recall the wordsfrom the list in any order (FIG. 10a ).

One goal was to analyze brain activity during study to derive estimatesof encoding efficiency for each individual word. To this end, a logisticregression classifier was trained to discriminate between patterns ofbrain activity predictive of successful memory (remembering learnedinformation following the delay) and those predictive of unsuccessfulmemory (forgetting of such information; FIG. 10b ). A subset of subjects(N=23) performed additional sessions of the memory task in which weapplied targeted electrical stimulation during encoding. For eachsession stimulation was applied across a single pair of electrodes,selecting contacts which showed a significant SME in the high-frequencyrange, a marker of successful memory encoding in iEEG. Stimulation toMTL and prefrontal cortical structures known to be critical for memoryencoding was targeted and stimulation parameters that have previouslybeen linked to improvements in spatial memory function in humans wasused. Each patient's unique classifier trained on the record-onlysessions was used to decode encoding efficiency from neural activityduring stimulation sessions.

Subjects completed at least 25 unique free recall lists during each ofthe record-only sessions (mean recall=27.4±2.1%; standard error of themean, SEM; FIG. 10a ). To train the classifier, spectral decompositionof the iEEG signal recorded at each electrode was used to estimate powerin time-frequency space for each word presentation period (1600 ms; FIG.10b , top panel). The power spectrum was then averaged across the timedimension and sorted individual word events into recalled and forgottenbins. Leave-one-out cross validation by word list was used to train theclassifier to discriminate recalled patterns from forgotten patterns.Area under the curve (AUC) was used to assess classifier performance andthe AUC values were compared for each subject to a subject-specific nulldistribution. Data from two example subjects are shown in FIG. 11a-c(Patient 1) and FIG. 11d-f (Patient 2). Across the group, classificationperformance was significantly higher than chance (0.54 vs 0.50,431)=5.2, P<0.0001 permutation test, FIG. 11g ), which was critical toestablish the feasibility of using the classifier to decode encodingefficiency. Classifier weights were largest in hippocampus, occipitalcortex, MTL and prefrontal cortex (FIG. 11h ), areas targeted in laterstimulation sessions. Classification was also performed using thedistribution of spectral power across electrodes within individualtime-frequency bins (FIG. 11i ), which suggested classifier performancewas highest in high-frequency, low 0 (1-3 Hz) and high 0 (4-8 Hz) rangesbeginning at 500 ms post-word onset.

The classifier output for a given input observation reflects the model'sconfidence in its classification decision, i.e. that the observationbelongs to the recalled vs. forgotten category. If this is true,subjects should be more likely to recall a word if the classifier ishighly confident it will be recalled than if it is less confident. Totest this, actual subject memory performance was calculated as afunction of the classifier's output for each trial. The cross-validationprocedure entailed testing the classifier on all encoded words, yieldingan encoding efficiency estimate for each word. The estimates from allwords from all sessions for that subject form a distribution over levelsof classifier estimates of encoding efficiency. This distribution wassorted into terciles and then subjects' recall performance varied acrossbins was evaluated. For each tercile, recall performance was computedfor words in the tercile, the subject's overall mean recall performancewas subtracted and this difference was divided by the overall mean(Supplemental Methods). This is a normalized measure that estimates theclassifier's effectiveness in assigning recalled words to the highesttercile and forgotten words to the lowest tercile. (FIG. 11c,f ). Meanrecall performance was significantly higher for words in the hightercile of classifier output compared to the low tercile (High:18.7±4.9%, Low: −18.5±4.1%; t(31)=4.4, P<0.0002; FIG. 11j ). Thissuggested that classifier output could be used as a continuous estimateof encoding efficiency suggesting the classifier could be used topredict stimulation's effect on memory.

In the stimulation sessions, bipolar stimulation was applied to a singlepair of electrodes in alternating blocks during the encoding phase ofeach list. Each block lasted for a pair of words so that each list of 12words included three pairs of stimulated words and three pairs ofunstimulated words. The experimental design for the stimulation sessionsalso included several word lists during which no stimulation was applied(NoStim lists), which was important as a baseline for behavioralperformance, and for testing the generalization of the classifiertrained on the previous record-only sessions.

Classifier AUC for NoStim lists was significantly greater than chance(0.53 vs. 0.50, P=0.05, permutation test), showing that patterns ofbrain activity associated with successful memory during the record-onlysessions predicted memory performance in the independent stimulationsessions. Memory performance for NoStim lists was similar to performanceduring the previous record-only sessions (mean recall 26.8±1.9%,P>0.20). Memory performance on NoStim lists was used as a baseline forassessing the overall behavioral effects of stimulation. Recall did notsignificantly differ for stimulated words relative to the NoStimbaseline (25.4±1.7%; P>0.25). There was also no evidence thatstimulation interfered with recall of non-stimulated words on the samelist (26.7±2.1%; P>0.23). Stimulation did not affect the serial positioncurve nor the lag-conditional response probability curve, twotraditional assays of performance in the study of human memory (FIG. 12a). These findings demonstrated little to no effect of stimulation onmemory performance across the group.

Although stimulation did not have consistent memory effects across thegroup, individual subject data suggested stimulation sometimes had quitestrong effects (FIG. 12b ). This might be because stimulation stronglymodulated neural activity in some subjects and had no effect in others.To test this, each subject's classifier was used to estimate encodingefficiency for words encoded immediately after stimulation offset.Average encoding efficiency was also computed for matched epochs in theNoStim lists and this baseline was subtracted from the stimulationcondition. For subjects with elevated encoding efficiencypost-stimulation, recall performance was likely to be increased whilethe reverse was true for subjects with decreased encoding efficiency(425)=0.33, FIG. 12c solid line). A permutation procedure was used toconfirm that this effect was not driven by variability in classifieroutput across subjects, which should correlate with memory performanceif the classifier shows above chance performance (FIG. 12c dashed line,P<0.02).

To understand how stimulation might enhance or depress memory function,some embodiments consider whether stimulation might be more likely topositively affect memory encoding if it is delivered when the brain isin a low-efficiency encoding state. To test this idea the classifier wasused to estimate encoding efficiency for the intervals just prior tostimulation delivery (FIG. 10c ) and the distribution was sorted againacross trials into high and low terciles. Subjects' memory performancewas then analyzed for words following stimulation offset (i.e. two listpositions later), conditioned on the classifier's estimate of theencoding state just prior to stimulation (FIG. 13). Stimulationcondition significantly interacted with encoding state (F(1, 28)=4.58,P<0.05) such memory was higher than the NoStim condition if stimulationarrived when encoding efficiency was poor (t(28)=1.92, P=0.06), but waslower if stimulation arrived when encoding efficiency was high. Theslope of the behavioral effect of stimulation was also negative acrossterciles (−12.4±9.5%) and was marginally different from the positiveslope observed for the non-stimulated condition (18.8±10.1%, t(28)=2.01,P=0.054).

The data showed that neural activity collected during encoding ispredictive of later memory performance. The data showed thatmultivariate patterns of neural activity that reflect encodingefficiency can be modulated using targeted electrical stimulation. Theextent to which stimulation affected activity in the encoding networkwas predictive of the extent to which stimulation affected memoryperformance. The data showed that changes in memory performance werecorrelated with stimulation-induced changes brain physiology, and linkedstimulation's effects to the brain state at the time it was applied.This demonstrates that direct perturbation of encoding activity leads topredictable changes in memory performance.

It was shown that multivariate statistical decoding of neural activitycan be used to infer brain states that are associated with successfulmemory encoding, and that such decoding can also be applied to predictthe behavioral effects of electrical stimulation. Although the decodingof neural activity was conducted in an offline post-processing stage, anatural extension of this work would be to implement multivariatedecoding in a closed-loop system to guide stimulation in real-time.Closed-loop approaches have been applied to the training of attentionusing fMRI and to maximizing the benefit of restudy events using scalpEEG. By showing that iEEG stimulation is most likely to improve memorywhen neural encoding efficiency is low just prior to stimulationdelivery, the work provides the foundation for future closed-loopapproaches to apply stimulation when it is likely to be most effectivein improving memory encoding. Future therapies may be able to exploitneural decoding in real-time to optimally stimulation to treat thesymptoms of memory disorders.

Methods Participants.

Patients undergoing intracranial electroencephalographic monitoring aspart of clinical treatment for pharmacologically-resistant epilepsy wererecruited to participate in this study. Data were collected as part of amulti-center study designed to assess the effects of electricalstimulation on memory-related brain function. Data were collected at thefollowing centers: Thomas Jefferson University Hospital (Philadelphia,Pa.), Hospital of the University of Pennsylvania (Philadelphia, Pa.),Mayo Clinic (Rochester, Minn.), Dartmouth Medical Center (Hanover,N.H.), Emory Hospital (Atlanta, Ga.) and University of TexasSouthwestern Medical Center (Dallas, Tex.). The research protocol wasapproved by the IRB at each hospital and informed consent was obtainedfrom the participants and their guardians.

Electrophysiological data were collected from electrodes implantedsubdurally on the cortical surface as well as deep within the brainparenchyma. In each case, the clinical team determined the placement ofthe electrodes so as to best localize epileptogenic regions. Subduralcontacts were arranged in both strip and grid configurations with aninter-contact spacing of 10 mm for surface contacts and 5 mm for depthcontacts.

Anatomical Localization.

Anatomical localization of electrode placement was accomplished using atwo step process. First, hippocampal subfields and MTL cortices wereautomatically labeled in a pre-implant T2-weighted MM using theautomatic segmentation of hippocampal subfields (ASHS) multi-atlassegmentation method. This dedicated T2-weighted sequence provides tissuecontrast at anatomical boundaries between hippocampal subfields and MTLcortical subregions, enabling automatic delineation of these areas.Following this automatic labeling procedure, a post-implant CT scan wasco-registered with the MRI using Advanced Normalization Tools.Electrodes that are visible in the CT were then localized within the MTLsubregions by a pair of expert neuroradiologists, specializing in MTLanatomy. The neuroradiologists confirmed the output of the automatedpipeline, and provided additional detail on localization withinsubfield/subregions.

Electrophysiological Recordings and Data Processing.

Intracranial data were recorded using one of the following clinicalelectroencephalogram (EEG) systems (depending the site of datacollection): Nihon Kohden EEG-1200, Natus XLTek EMU 128 or GrassAura-LTM64. Depending on the amplifier and the preference of theclinical team, the signals were sampled at either 500 or 1000 Hz andwere referenced to a common contact placed either intracranially, on thescalp or mastoid process. A 5 Hz band-stop first order Butterworthfilter around 60 Hz was applied to remove signal from electrical linenoise. A bipolar referencing montage was then generated for each subjectby taking the difference between the voltage timeseries recorded at allpairs of spatially adjacent electrodes. A spectral decomposition on thevoltage timeseries was computed for each bipolar-referenced signal. Forthe record-only sessions, wavelet decomposition was used to extractpower across a set of 50 logarithmically-spaced frequencies between 1and 200 Hz (wave number=7). Wavelet decomposition was computed for each1600 ms word encoding epoch, with an additional 1500 ms buffer periodbefore and after that was discarded to remove convolution edge effects.The resulting time-frequency data were then averaged into larger 100 mstime bins, with 100 ms spacing.

Verbal Memory Task.

Each subject participated in a delayed free-recall task in which theywere instructed to study lists of words for a later memory test; noencoding task was used. Lists were composed of 12 words chosen at randomand without replacement from a pool of high frequency nouns (eitherEnglish or Spanish, depending on the participant's native language;http://memory.psych.upenn.edu/WordPools). Each word remained on thescreen for 1600 ms, followed by a randomly jittered 750-1000 ms blankinter-stimulus interval (ISI).

Immediately following the final word in each list, participantsperformed a distractor task (20 seconds) consisting of a series ofarithmetic problems of the form A+B+C=??, where A, B and C were randomlychosen integers ranging from 1-9. Following the distractor taskparticipants were given 30 seconds to verbally recall as many words aspossible from the list in any order; vocal responses were digitallyrecorded and later manually scored for analysis. Each session consistedof 25 lists of this encoding-distractor-recall procedure. Eachparticipant performed at least two sessions of the passive recordingversion of the verbal memory task, and two sessions of the stimulationversion of the task.

Stimulation Methods.

For each stimulation session, electrical current was passed through asingle pair of adjacent electrode contacts. At the start of eachsession, a safe amplitude was determined for stimulation using a mappingprocedure in which stimulation was applied at 0.5 mA while a neurologistmonitored for after discharges.

In some embodiments, this procedure was repeated, incrementing theamplitude in steps of 0.5 mA, up to a maximum of 1.5 mA for depthcontacts and 3.5 mA for cortical surface contacts. These maximumamplitudes were chosen to be well below accepted safety limits forcharge density. Stimulation was delivered using charge-balanced biphasicrectangular pulses (pulse width=300 μs) at 50 Hz frequency. During theencoding phase, stimulation was applied continuously for 4.6 s whilesubjects encoded two consecutive words; stimulation was not applied forthe following two; and this alternation procedure continued until theend of the list. Each 4.6 s block of stimulation began 200 ms prior tothe presentation of a word on the screen and lasted until 200-450 msafter the offset of the next word. Stimulation was applied in 20 of the25 lists in a session, and each stimulation list was randomly chosen tobegin with a stimulated or non-stimulated pair of words. The order ofthe 20 stimulation lists and 5 non-stimulation lists was randomizedwithin each session. The non-stimulation lists served as a baseline forthe analysis of behavioral data and intracranial recordings.

Machine Learning Classification.

Spectral power averaged across the time dimension was used for each wordencoding epoch as the input to a logistic regression classifier. Thus,the features for each individual word encoding observation were theaverage power across time, at each of the 50 frequencies at eachelectrode. L2-penalization and a cross-validation approach was used toselect the optimal penalty parameter before applying this parameterduring our final cross-validated model training. To select the penaltyparameter, a set of 25 logarithmically-spaced penally parameters wasgenerated from 100 to 5000. For each possible penalty parameter,five-fold leave-one out cross-validation was computed to generate aclassifier probability estimate for each word encoding observation. Areceiver operating characteristic (ROC) curve was then computed for theset of classifier estimates derived from testing each penalty parameterin each crossvalidation fold. The penalty parameter that yielded thelargest mean AUC over cross-validation folds was chosen for use in finalmodel training and testing.

For the stimulation sessions, the spectral decomposition was computedfor the −900 to −100 ms period prior to onset of each stimulation eventusing the multitaper method. The bipolar referenced voltage signal wasprojected onto a set of three orthogonal Slepian windows (2 Hzbandwidth) before decomposition with the Fast Fourier Transform.Zero-padding of the signal was used to achieve the desired frequencyresolution. A window size of 400 ms was used and the multitaper estimatewas computed at each sample in the 800 ms period of interest. Powerwithin desired frequencies was averaged across all estimates from the800 ms pre-stimulation period, for all electrodes in the subject'smontage. This set of features (50 frequencies×N electrodes) was thenused as a set of observations and input to a logistic regressionclassifier that had been trained on data from the record-only period.This produced a model-derived estimate of the probability that the brainwas in an efficient encoding state just prior to stimulation onset. Thepreceding analysis for the 100 to 2100 ms period was conducted followingstimulation offset. Electrical artifacts precluded analysis of thestimulation interval itself.

Behavioral Analysis.

The percentage of words recalled from the encoding lists was used as abehavioral measure of memory performance. Within the experimentaldesign, memory performance was determined in four conditions:record-only words, stimulated words, non-stimulated words encoded in thesame list as stimulated words, and non-stimulated words encoded innon-stimulated lists (NoStim). The overall effects of stimulation onmemory was analyzed by comparing recall performance for Stim words toNoStim words using two standard measures, the serial position curve(SPC; FIG. 12a , top) and the lag conditional response probability curve(CRP; FIG. 12a , bottom). The SPC depicts the probability of recalling astudied word as a function of its position within the study list. TheCRP depicts the probability of recalling two words from the study listas a function of the lag (in number of words) between the serialpositions of the two words.

Analysis Stimulation's Effect on Memory Performance.

Because electrical artifacts evoked by our stimulation trains precludeddirect analysis of the stimulation epochs themselves, the analysis ofthe relationship between stimulation and memory performance focused onepochs prior and following the stimulation trains. To quantify theeffect of stimulation on encoding efficiency during these periods,estimates of encoding efficiency were computed using a multivariatemodel for pre-stimulation and post-stimulation periods. An inverse logictransform was used to convert the classifier estimates on thesemeasures. The same analysis was conducted for matched epochs withinNoStim lists, which served as our baseline measure of encodingefficiency for each patient. A percent change measure

${{\Delta\;{EE}} = {100*\frac{{EE}_{Stim} - {EE}_{NoStim}}{{EE}_{NoStim}}}},$was computed where EE_(X) is the mean encoding efficiency across trialsin condition X. This measure accounts for differences in subjects'baseline levels of recall performance.

A similar measure was computed to quantify the stimulation-relatedchange in recall relative to NoStim baseline for words encodedimmediately following stimulation offset:

${{\Delta\;{RR}} = {100*\frac{{RR}_{{Post}\text{-}{Stim}} - {RR}_{{Post}\text{-}{NoStim}}}{{RR}_{{Post}\text{-}{NoStim}}}}},$where RR_(X) refers to the recall rate in condition X. We computed theacross-subject correlation between ΔEE and ARR (FIG. 12c ) and assessedsignificance using a bootstrapping procedure. Within each of 1000permutations, the Stim/NoStim labels were randomly shuffled at theword-level within-subject. ΔEE and ΔRR was computed on the permuted datafor each subject before computing the across-subject correlation betweenthese measures on the permuted data. A p-value was derived by comparingthe true Pearson's r with the distribution of r-values generated by thepermutation procedure.

To assess the effects of stimulation on recall rate as a function of thedegree of encoding efficiency just prior to stimulation delivery, anormalized measure of memory was used for the post-stimulation words,ΔRR, but split trials into terciles based on each subject's distributionof prestimulation EE values. ΔRR was analyzed as a function of tercile(lowest/highest) and stimulation condition (Stim/NoStim) using a 2×2analysis of variance (FIG. 13).

Attached herewith is an Appendix that illustrates one or moreembodiments of the present inventions described herein.

In at least one embodiment, there is included one or more computershaving one or more processors and memory (e.g., one or more nonvolatilestorage devices). In some embodiments, memory or computer readablestorage medium of memory stores programs, modules and data structures,or a subset thereof for a processor to control and run the varioussystems and methods disclosed herein. In one embodiment, anon-transitory computer readable storage medium having stored thereoncomputer-executable instructions which, when executed by a processor,perform one or more of the methods disclosed herein.

It will be appreciated by those skilled in the art that changes could bemade to the exemplary embodiments shown and described herein withoutdeparting from the broad inventive concept thereof. It is understood,therefore, that this invention is not limited to the exemplaryembodiments shown and described, but it is intended to covermodifications within the spirit and scope of the present invention asdefined by the claims. For example, specific features of the exemplaryembodiments may or may not be part of the claimed invention and featuresof the disclosed embodiments may be combined. Unless specifically setforth herein, the terms “a”, “an” and “the” are not limited to oneelement but instead should be read as meaning “at least one”.

It is to be understood that at least some of the figures anddescriptions of the invention have been simplified to focus on elementsthat are relevant for a clear understanding of the invention, whileeliminating, for purposes of clarity, other elements that those ofordinary skill in the art will appreciate may also comprise a portion ofthe invention. However, because such elements are well known in the art,and because they do not necessarily facilitate a better understanding ofthe invention, a description of such elements is not provided herein.

Further, to the extent that the method does not rely on the particularorder of steps set forth herein, the particular order of the stepsshould not be construed as limitation on the claims. The claims directedto the method of the present invention should not be limited to theperformance of their steps in the order written, and one skilled in theart can readily appreciate that the steps may be varied and still remainwithin the spirit and scope of the present invention.

What is claimed is:
 1. A method for delivering stimulation to alter acognitive state of a user, the method comprising: during a testingphase: presenting a plurality of test items to the user; receiving aplurality of responses and a plurality of testing phase brain signalsfrom the user; associating each of the plurality of responses and eachof the plurality of the testing phase brain signals with each of theplurality of test items; determining that each of the testing phasebrain signals corresponds to one of: a high-performance cognitive stateof the user and a low-performance cognitive state of the user, using theassociated test item and associated response; and in response todetermining that each of the testing phase brain signals corresponds toone of: a high-performance cognitive state of the user and alow-performance cognitive state of the user, generating a testing phasebiomarker using the plurality of testing phase brain signals thatcorresponds to the one of: the high-performance cognitive state of theuser and low-performance cognitive state of the user, wherein thetesting phase biomarker is a representation of electrophysiologicaldata; during a stimulation phase occurring after the testing phase:monitoring a stimulation phase brain signal from the user; evaluatingthe cognitive state of the user by comparing the stimulation phase brainsignal of the user with the testing phase biomarker; deliveringstimulation to a brain of the user based on the evaluating step to steerthe brain of the user towards one of: the high-performance cognitivestate of the user and the low-performance cognitive state of the user.2. The method of claim 1, wherein the testing phase biomarker is a setof one or more features that distinguish a respective brain signal thatcorresponds to the high performance cognitive state from a respectivebrain signal that corresponds to the low performance cognitive state ofthe user.
 3. The method of claim 1, wherein the testing phase biomarkeris associated with a threshold corresponding to a feature of arespective brain signal.
 4. The method of claim 1, further comprising:transmitting the testing phase biomarker to a modulation device.
 5. Themethod of claim 1, further comprising: receiving the testing phase brainsignals from a modulation device connected to the brain of the user. 6.The method of claim 1, further comprising: associating the plurality ofresponses with the plurality of test items by comparing the plurality ofresponses to the plurality of test items.
 7. The method of claim 1,further comprising: associating the testing phase brain signal with theplurality of test items by determining whether a time period where thetesting phase brain signals are monitored by a modulation deviceoverlaps with a time period where the plurality of test items ispresented to the user.
 8. The method of claim 1, wherein if the responsematches the plurality of test items, the response is a positiveresponse.
 9. The method of claim 1, wherein the testing phase brainsignal corresponds to the high-performance cognitive state of the userif the testing phase brain signals are associated with plurality of testitems having a positive response.
 10. The method of claim 1, wherein ifa respective response does not match the plurality of test items, therespective response is a negative response.
 11. The method of claim 1,wherein the testing phase brain signals corresponds to the lowperformance cognitive state of the user if the brain signals areassociated with a plurality of test items having a negative response.12. The method of claim 1, wherein the high performance cognitive stateis an accurate memory.
 13. The method of claim 1, wherein the lowperformance cognitive state is an inaccurate memory.
 14. The method ofclaim 1, further comprising: storing the testing phase biomarker in adatabase.
 15. The method of claim 1, wherein the testing phase brainsignals are one or more brain signals from one or more users.
 16. Themethod of claim 1, further comprising: generating a biomarkerrepresentative value by applying a dimensionality reduction algorithm toone of: the testing phase biomarker and the testing phase brain signals.17. The method of claim 1, further comprising: optimizing stimulationparameters by applying stimulation to the user using a plurality ofselected parameters and selecting one or more of the plurality ofselected parameters that causes a desired effect on the user.
 18. Asystem for creating a biomarker indicative of high performance or lowperformance cognitive state according to the method of claim
 1. 19. Anon-transitory computer readable storage medium having stored thereoncomputer executable instructions which, when executed by a processor,perform any of the methods for creating a biomarker indicative of highperformance or low performance cognitive state according to the methodof claim
 1. 20. The method of claim 1, wherein the testing phasebiomarker is indicative of the low performance cognitive state of theuser as determined based on a cognitive test performed on a contributor.21. The method of claim 1, wherein the testing phase biomarker isindicative of a high performance cognitive state of the user asdetermined based on a cognitive test performed on a contributor.
 22. Themethod of claim 20, wherein the contributor is the user.
 23. The methodof claim 20, wherein the contributor is a plurality of contributors. 24.The method of claim 20, wherein the contributor is different than theuser.
 25. The method of claim 1, further comprising: updating thetesting phase biomarker based on the testing phase brain signal of theuser and a response of the user to the plurality of test items.
 26. Themethod of claim 1, wherein electrical stimulation is delivered to asingle subfield of a hippocampus.
 27. The method of claim 1, whereinelectrical stimulation is delivered to multiple regions of the brain ofthe user.
 28. The method of claim 1, wherein comparing the stimulationphase brain signal to the testing phase biomarker includes comparing thestimulation phase brain signal to a biomarker representative valuegenerated by applying a dimensionality reduction algorithm to thetesting phase biomarker.
 29. The method of claim 1, wherein stimulationdelivered to the brain of the user is based on predetermined stimulationoptimization parameters derived before the monitoring step andconfigured to steer the brain of the user towards high performancecognitive state.